A machine learning approach to predict meat production factors in Japanese black cattle fattening

A machine learning approach to predict meat production factors in Japanese black cattle fattening | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A machine learning approach to predict meat production factors in Japanese black cattle fattening Daniele Pinna, Pablo Guarnido-Lopez, Shin-ichi Nagaoka, Moriyuki Fukushima, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6653315/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Thanks to its unique characteristics, such as the level of marbling, Japanese Wagyu beef is considered one of the highest quality meats in the world. These characteristics are the result of a wide variety of factors including genetics, production systems, diets, breeding techniques, and environmental conditions. However, the farmer's profit is strongly related to the cost of production (especially the cost of feeding), to the final weight of the carcass and to the quality score that each animal obtains at the slaughterhouse. For this reason, this study aimed to build, test, and optimize a machine learning algorithm for the prediction of individual carcass traits, which could be used by farmers as a support tool. To achieve this result, data on environmental conditions, behavior, and blood composition were obtained from 44 Japanese black cattle raised for beef production. The obtained databases were then optimized through two techniques of feature selection: genetic algorithm and correlation analysis. For each of the resultant databases, a neural network was built and tested. The results showed a promising ability of machine learning algorithms to predict carcass traits with an acceptable accuracy even with a small sample size. Especially, the genetic algorithm optimized database resulted in the best solution, obtaining a higher accuracy (R 2 =0.34) with respect to the complete database (R 2 =0.27) and the correlation optimized database (R 2 =0.09). This study provides a first step forward in the use of machine learning techniques for the optimization of Wagyu beef production. Physical sciences/Mathematics and computing/Scientific data Physical sciences/Mathematics and computing/Computer science Machine learning Japanese Wagyu Feature selection Precision livestock farming Decision Support System Meat quality Figures Figure 1 Figure 2 Introduction The consumption of animal-based protein and animal products is expected to increase by up to 50% by 2050 [ 1 ]. Consequently, to fulfill this increasing demand the number of livestock is increasing but the number of farmers and livestock farms are decreasing [ 2 ]. This phenomenon is leading to a great intensification of livestock farms, reducing the ability of farmers to adequately monitor all the animals [ 3 ]. For this reason, in modern livestock farming, the use of automatic sensors and data analysis systems is becoming prominent, thanks to the introduction of Precision Livestock Farming (PLF). This concept is defined as “individual animal management by continuous real-time monitoring of health, welfare, production, reproduction, and environmental impact” [ 4 ]. The spreading of PLF technology has been made possible thanks to technological advancements (i.e., communication technologies, IoT) and the reduced cost and dimension of electronic devices [ 5 ]. However, currently, PLF is mostly applied in intensive farms, while in small-scale, extensive, or outdoor systems most of the PLF technologies are considered less profitable or even impossible to apply [ 6 ]. Nevertheless, one exemption could be represented from animal production with high market value, such as the Japanese “Wagyu” beef. The Wagyu production system is mostly composed of small-scale farms with a national average of 69.6 heads per farm [ 7 ]. However, thanks to its consideration as a “gourmet” food, Wagyu beef can reach very high prices even in the domestic market (6550–13290 yen/kg, 42.12–85.47 euro/kg) with a higher price for specific cuts or exported product [ 8 ]. The most relevant characteristic for the fame and appreciation of Wagyu beef is the “Marbling” or the ability of the animal to have a high deposition of intramuscular fat, which contributes to the juiciness, flavor, and tenderness of the meat [ 9 ]. To regulate Wagyu beef production, the Japan Meat Grading Association (JMGA; www.jmga.or.jp ) established a standard for beef quality evaluation. According to this standard, the carcass is judged based on the surface of the 6th /7th rib cross-section. Then, two summarized indices are calculated to include the meat into 15 categories: Yield Grade (YG) and Quality Grade (QG). YG is calculated by the equation: $$\:YG=67.37+\left(0.130RYA\right)+\left(0.667RT\right)-\left(0.025SCW\right)-\left(0.869SFT\right)+2.049$$ where 67.37 represent the basic Yield Grade Score, RYA corresponds to the rib-eye area, RT to rib thickness, SCW to the cold split carcass weight, SFT to the subcutaneous fat thickness and 2.049 is constant included. Then, YG can be classified into 3 grades: A (above average yield or > 72), B (average yield or between 69 and 72), and C (below average yield or < 69). Moreover, the QG is based on 4 characteristics: Beef Marble Standard (BMS), Beef Color Standard (BCS), meat texture and firmness and Beef Fat Standard (BFS). It is classified into 5 grades: from 5 (highest quality) to 1 (lowest quality; Japan Meat Grading Association, 2023). The score that the carcass receives in the meat evaluation process will strongly influence the price that the final product could reach in the market. For this reason, animals in the fattening period until slaughter (from 10 to 30 months of age) follow a high-concentrate diet, which can reach up to 86% of the total ingestion in the final fattening stage [ 11 ]. Another important factor in achieving a higher level of marbling is serum vitamin A (Vit.A) management. In fact, maintaining a low level of serum Vit.A, especially during the early and middle stages of fattening, shows positive results in the accumulation of intramuscular fat in Japanese cattle breeds [ 12 ]. Although Vit.A deficiency in the diet is acceptable, it could induce poor weight gain, ataxia, blindness, and even death in serious cases [ 13 ]. Furthermore, intramuscular fat deposition is not only influenced by Vit.A but it is also the consequence of a complex series of variables including sex, genetics, nutrition, and management factors such as weaning/slaughter age, castration, and environmental conditions [ 14 ]. Thanks to the recent advance of machine learning (ML), it may be possible to analyze all possible factors influencing the meat quality of Wagyu beef and to create an instrument for farmers to determine the most beneficial relationships between these factors achieving the best trade-off between meat quality and animal performance on the feedlot. Moreover, connecting ML to modern PLF sensors could improve the capability of farmers to interpret the large quantity of data obtained by smart devices. These data can contain important information or patterns that ML is more efficient in recognizing. ML has already shown great potential in the prediction of carcass characteristics in beef and sheep (Shahinfar et al., 2019; Aiken et al., 2020; Monteiro et al., 2024). It is also important to consider that in highly fragmented production systems, such as the Wagyu beef industry, it is very difficult to obtain constant and frequent measurements on a large number of animals. For this reason, this study aimed to build, test, and optimize an ML model, based on a reduced number of animals, for the prediction of the Japanese meat quality standards using a collection of data obtained by PLF smart sensors. Materials and Methods Ethical approval for animal trials This study was carried out in strict compliance with the regulation of animal experiments at Kyoto University in the Guide for the Care and Use of Laboratory Animals. The regulation was approved by the Kyoto University Animal Experimentation Committee. The study did not involve endangered or protected species. All efforts were made to minimize animal suffering. The study is reported in accordance with the ARRIVE guidelines. Animals A total of 44 clinically healthy Japanese black cattle from Tajima Agricultural High School (Yabu, Japan) were involved in the study. These animals were part of several studies from 2018 to 2023. The animals arrived at the fattening facilities after a weaning period of around 270 days. Animals were fed with a traditional low b-carotene diet, composed mainly of concentrate and an ad libitum access to oaten. In each pen, animals had access to an individual door feeder, water, and minerals. The ground was covered in sawdust which was cleaned monthly. After the completion of the fattening process, animals were driven to the Kakogawa city abattoir to be slaughtered. Data The body weight of each animal was evaluated through a calibrated electronic weight scale, every ∼30 days. Moreover, each animal was equipped with a U-motion (Desamis Ltd., Tokyo, Japan) that measured the minimum (min), average (avg), and maximum (max) levels of temperature, humidity, and temperature humidity index (THI) every 24 hours. The U-motion also measured the time (in minutes) that each animal spent in different activities during the 24 hours. These activities were feeding (Feed_time), moving (Move_time), lying (Lie_time), lying rumination (lying_rumination_time), standing (stand_time), and standing rumination (standing_rumination_time). Finally, each animal was strictly monitored to prevent hypovitaminosis A through practical checking techniques and blood samples. The blood samples were collected every ∼60 days via jugular venipuncture and the blood composition was measured with high-performance liquid chromatography at the Wadayama Service Center of Hoken Kagaku (Asago, Japan). The measured components of the blood included vitamin A (Vit.A), aspartate aminotransferase (AST), alanine transaminase (ALT), g-glutamyl transferase (GGT), and total cholesterol (T-Cho). A summary of the carcass traits statistics of animals included in the study can be found in Table 1. Table 1. Summary statistics for carcass traits of animals involved in the study. Training set Test set Initial Database Traits Mean Min Max Mean Min Max Mean Min Max Final weight (kg) 642.0 461.0 762.0 655.8 536.0 714.0 645.7 461.0 762.0 Carcass weight (kg) 424.0 352.0 486.0 421.5 357.5 470.6 423.3 352.0 486.0 Yield grade 74.3 70.7 77.2 73.9 71.8 75.5 74.1 70.7 77.2 Quality grade 4.7 4.0 5.0 4.7 4.0 5.0 4.6 4.0 5.0 Rib-eye area (cm 2 ) 59.9 41.0 77.0 56.7 46.0 69.0 59.0 41.0 77.0 Beef rib thickness (cm) 7.2 5.9 9.4 7.3 5.7 8.3 7.2 5.7 9.4 Subcutaneous fat thickness (cm) 2.7 1.6 4.1 2.8 2.0 4.6 2.7 1.6 4.6 Beef Marble Standard (BMS) 8.8 6.0 12.0 8.8 6.0 12.0 8.7 6.0 12.0 Beef Color Standard (BCS) 4.0 3.0 5.0 3.8 3.0 4.0 3.9 3.0 5.0 Meat gloss 4.7 4.0 5.0 4.7 4.0 5.0 4.7 4.0 5.0 Meat firmness 4.7 4.0 5.0 4.7 4.0 5.0 4.7 4.0 5.0 Meat texture 4.8 4.0 5.0 4.7 4.0 5.0 4.8 4.0 5.0 Beef Fat Standard (BFS) 2.5 2.0 4.0 2.3 2.0 3.0 2.4 2.0 4.0 Neural Network The Artificial Neural Network model (ANN) was built, tested, and optimized with the use of the AI-assisted platform Multi-Sigma (Aizoth Inc., Tsukuba, Japan; Aizoth Inc., 2023) and RStudio (ver. 2023.12.1 build 402; RStudio Core Team, 2018). Furthermore, the structure of the ANN is optimized to reduce overfit and perform analysis with small data sets thanks to the use of AI algorithms that automatically select hyperparameters. This aspect represents a clear advantage for the application of ANN in highly fragmented scenarios and with a limited use of technological tools, where collecting data from a large number of animals is impractical and economically challenging. However, the dataset limitation was carefully weighted to ensure that the study remains robust and provides meaningful insights, and the data limitation was accorded with “The Three Rs” guiding principle (Russel & Burch, 1959) for appropriate animal experiments. Moreover, 30% of the database, randomly selected, was excluded from the training set and used as an independent test set. The remaining 70% was considered as the training set and fed to the application. At the end of the training, 5 ANN models were created automatically selecting the structure’s parameters based on the input data, randomly selecting each time 90% of data for training and 10% for validation. This further segmentation of the data is specifically designed to reduce the overfit, because the configuration ANN is automatically selected to minimize the error between training and validation sets. Also, to improve the prediction performances, all the ANN models were used simultaneously. Furthermore, based on the size and nature of the datasets, two methods of feature selection were tested for optimization: Genetic algorithm (GA; Shapiro, 2001): The GA is a technique for optimization based on natural selection over multiple generations. It was performed on the “yield grade” and “quality grade” results because they included all the other factors of the meat quality evaluation. The hyperparameters used were population size = 400, generation = 200, crossover rate = 0.1, mutation rate = 0.3, evaluator = Adjusted R 2 . Correlation (CORR): Pearson’s correlation was used to select the predictor features that had a higher correlation (absolute value > 0.5) with one of the objective features of the database (output of the model). The feature selection was performed with the RStudio using the package “gaselect” and the base function “COR”. Predictor features selected by each method are shown in Table 2. In “Complete” (COMPLETE) feature selection was not performed. The process of feature selection is very important to optimize the training time, and the data collection time and to reduce the possible negative impact of irrelevant features in the database. The accuracy measurements for model evaluation were the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), and the R-squared coefficient (R 2 ). A summary of the data analysis process is shown in Fig. 1. Table 2 . Databases used in the study and predictor features, divided by type, selected by each optimization method. Complete (COMPLETE) Genetic Algorithm (GA) Correlation (CORR) Morphology and development Sex √ √ √ Initial_weight (kg) √ Monthly_weight_gain (kg) √ √ √ Age_min (month) √ √ Age_max (month) √ √ Environment Temperature_min (°C) √ Humidity_min (%) √ √ √ THI_min √ √ Temperature_avg (°C) √ Humidity_avg (%) √ √ THI_avg √ Temperature_max (°C) √ √ Humidity_max (%) √ THI_max √ √ √ Behavior Feed_time_total (min) √ Feed_time_avg (min) √ √ √ Move_time_total (min) √ √ Move_time_avg (min) √ Lie_time_total (min) √ √ Lie_time_avg (min) √ √ Stand_time_total (min) √ √ Stand_time_avg (min) √ √ √ Standing_rumination_time_total (min) √ Standing_rumination_time_avg (min) √ √ Lying_rumination_time_total (min) √ Lying_rumination_time_avg (min) √ √ Blood composition Vit.A_min ( μ g /dL) √ AST_min ( μg /dL) √ ALT_min ( μg /dL) √ GGT_min ( μg /dL) √ T-cho_min (μg /dL) √ Vit.A_avg (μg /dL) √ √ AST_avg ( μg /dL) √ √ ALT_avg ( μg /dL) √ GGT_avg ( μg /dL) √ √ T-cho_avg (μg /dL) √ Vit.A_max (μg /dL) √ AST_max ( μg /dL) √ ALT_max ( μg /dL) √ √ GGT_max ( μg /dL) √ T-cho_max (μg/dL) √ √ Total feature 41 17 11 Statistical analysis Statistical and mathematical analyses were performed in RStudio (ver. 2023.12.1 build 402). ANOVA (parametric variables) and Kruskal-Wallis test (non-parametric variables) were used to test differences in the ANN structure and performance and declared significant when P ≤ 0.05. Results and Discussion The totality of 15 (5 for each database) ANNs generated had similar results in terms of structure, with no statistical difference for any of the tuning hyperparameters (Table 3 ). This outcome can be due to the algorithm’s setting for parameter selection, but also to the fact that in ML the principal factor that influences the optimum structure of ANNs is the data nature and not the number of predictor features [ 22 ]. However, the models trained with the GA selected features appeared numerically more complex than the others, especially in terms of neurons per layer and epoch of training performed. Table 3 Summary of ANNs parameters generated by Multi-Sigma for each dataset. COMPLETE GA CORR Mean (SD) 7.6(1.67) a 9(1.73) a 8.2(2.05) a N° hidden layer Min 5 a 6 a 5 a Max 9 a 10 a 10 a Mean (SD) 52.2(34.28) a 59.6(30.98) a 35(32.96) a N° Neurons per layer Min 3 a 27 a 8 a Max 86 a 98 a 73 a Mean (SD) 5198.20(1924.66) a 7834.4(1834.25) a 5060.2(2130.73) a N° Epoch performed Min 2674 a 6372 a 2813 a Max 7718 a 9848 a 7382 a a−b Different superscript letters indicate statistical differences among values in the row (P≤ 0.05) In general, the application of AI-assisted machine learning yielded very promising results, especially considering the sample size included in the study. Table 4 presents the performance of the models on the independent test set for predicting all 13 traits of the Japanese beef grading system. All the ANNs performed similarly, but the COMPLETE database and GA selected features outperformed the CORR based database in 11 of 13 trait predictions, showing a higher accuracy. However, for each of the carcass traits, these models performed differently, which may be due to their different model´s construction itself. The COMPLETE models showed higher accuracy, especially in the yield grade score (RMSE = 0.92, MAE = 0.71, R 2 = 0.50), the final weight score (RMSE = 43.29, MAE = 31.94, R 2 = 0.23) and rib-eye area score (RMSE = 6.86, MAE = 5.14, R 2 = 0.43). This could be explained considering that this COMPLETE model integrated fundamental features for morphology prediction, such as initial weight, or some of the blood biomarkers. In fact, these three traits with better outcomes with the COMPLETE model are related to the initial weight as it is part of the modeling used to forecast the final weight and rib-eye area as previously demonstrated [ 23 ]. In the particular case of the final weight, when prediction models do not incorporate the initial weight, the performance is much less accurate [ 24 ]. The CORR models showed the best performance for the carcass weight (RMSE = 30.29, MAE = 22.45, R 2 = 0.09) and the beef rib thickness (RMSE = 0.90, MAE = 0.64, R 2 = 0.14). This may be due to the high phenotypic and genetic correlation (mostly linear) of these two parameters with the rest of the meat quality variables measured [ 25 , 26 ]. However, for the carcass weight, the R 2 is lower in comparison to the COMPLETE model which obtained similar performance in terms of RMSE and MAE (carcass weight: RMSE = 30.67, MAE = 22.89, R 2 = 0.17). A higher level of correlation between prediction and observed value could imply a higher possibility of generalization of the model and a consequent lower level of overfitting. Moreover, the GA model resulted to be the most effective in the prediction of quality grade (RMSE = 0.37, MAE = 0.27, R 2 = 0.76) and most of its components as BMS (RMSE = 1.65, MAE = 1.43, R 2 = 0.25), Meat gloss (RMSE = 0.37, MAE = 0.24, R 2 = 0.93), Meat firmness (RMSE = 0.39, MAE = 0.27, R 2 = 0.75), meat texture (RMSE = 0.40, MAE = 0.27, R 2 = 0.83) and BFS (RMSE = 0.45, MAE = 0.32, R 2 = 0.06). The GA model´s construction is related to the creation of new parameters through inputs explaining most of the variability of outputs, therefore, these better relationships observed in meat quality parameters could be due to the fact that Black Japanese cattle´s production system is focused on improving those meat quality parameters [ 27 ], which may explain the high level of the variability relating inputs and meat quality outputs. Especially, the good prediction ability of the BMS could represent a very interesting point for Wagyu beef production. In fact, this trait is the most influential on the Japanese beef grading system and is considered to be strongly connected to the animal’s diet [ 28 ]. However, the collection of feed intake information of individual animals is a challenging task on a farm and consequentially, being able to obtain a reliable prediction of BMS, without knowing the net feed intake, could represent an important advantage of ML system. Table 4 Prediction accuracy of the neural network model for predicting Japanese black cattle carcass characteristics for the Japanese beef grading system. Root Mean Square Error (RMSE); Mean Absolute Error (MAE); R-squared coefficient (R 2 ). A bold value denotes the best score in the row. Trait Scale or unit of measurement Metric COMPLETE GA CORR Final weight RMSE 43.92 56.54 55.15 kg MAE 31.94 38.61 37.45 R 2 0.23 0.0001 0.01 Carcass weight RMSE 30.67 34.97 30.29 kg MAE 22.89 25.07 22.45 R 2 0.17 0.005 0.09 Yield grade RMSE 0.92 1.34 1.46 1-100 MAE 0.71 1.03 1.33 R 2 0.50 0.13 0.0002 Quality grade RMSE 0.46 0.37 0.62 1–5 MAE 0.30 0.27 0.57 R 2 0.37 0.76 0.20 Rib-eye area RMSE 6.86 9.13 8.13 cm 2 MAE 5.14 7.46 7.17 R 2 0.43 0.07 0.14 Beef rib thickness RMSE 0.98 0.94 0.90 cm MAE 0.85 0.76 0.64 R 2 0.08 0.07 0.14 Subcutaneous fat thickness RMSE 0.60 0.51 0.61 cm MAE 0.53 0.42 0.47 R 2 0.22 0.57 0.20 Beef Marble Standard (BMS) RMSE 1.85 1.65 2.22 1–12 MAE 1.61 1.43 1.92 R 2 0.14 0.25 0.03 Beef Color Standard (BCS) RMSE 0.43 0.49 0.69 1–5 MAE 0.32 0.37 0.48 R 2 0.20 0.02 0.15 Meat gloss RMSE 0.45 0.37 0.60 1–5 MAE 0.28 0.24 0.54 R 2 0.53 0.93 0.13 Meat firmness RMSE 0.48 0.39 0.62 1–5 MAE 0.32 0.27 0.55 R 2 0.27 0.75 0.13 Meat texture RMSE 0.48 0.40 0.57 1–5 MAE 0.31 0.27 0.49 R 2 0.33 0.83 0.06 Beef Fat Standard (BFS) RMSE 0.49 0.45 0.50 1–5 MAE 0.34 0.32 0.43 R 2 0.01 0.06 0.0002 It is important to notice how the GA selection of features performed generally better than the COMPLETE database, while the CORR selected features performed worse than all the other models. These results confirm the ability of GA feature selection methods to allow an optimization of ML without reducing the accuracy of predictions. Another aspect to take into consideration is the “composition” of the prediction. Figure 2 shows the contribution of the different input features, divided by type, for the prediction of the two summarized scores of the Japanese beef grading system. These results reflect the actual knowledge that the final yield or quality of the meat is mostly influenced: (1) by the morphology and development of the animal, which are strongly influenced by his genetical traits, (2) by the behavior, which also includes the time spent in the feeding area and consequentially the ingestion rate, (3) and finally by the blood composition, which is mostly influenced by the breeding techniques and the composition of the feeding (Motoyama et al., 2016). In particular, from the factor analysis, it highlighted how the standing time (comprehensive of standing, standing rumination, and moving), initial weight, monthly weight gain, and the level of aspartate transferase strongly influenced the yield grade, while the quality grade and marbling score resulted more related to the moving time, the level of Vit. A, and the concentration of alanine transaminase. Thus, it is also very interesting to note how the environmental conditions showed a relevant influence on the quality and quantity of meat production. This work confirmed the ability of ML to predict the carcass traits of Japanese black cattle for Japanese beef grading systems with a good accuracy, taking into consideration that the limited sample size may affect especially the ability of generalization of the prediction model, with the risk of a high specificity and overfitting [ 29 ]. The composition of Wagyu beef is quite different from other breeds, so the development of breed specific grading algorithm could represent an interesting research area for improving the profitability of farms. Nevertheless, To take full advantage of the benefits derived from the use of ANN for Wagyu cattle management the integration into a complete software for animal data use is needed. Conclusion In this study, a machine learning model for the prediction of the quality of Wagyu beef was developed, tested, and optimized. In addition, three different methods of feature selection for the construction of neural networks were tested. The performances of the models resulted in a very promising prediction of the score of the Japanese meat grading standards. In particular, the model created with the complete database of 41 features showed a higher accuracy for the prediction of the yield grade (R 2 =0.50) while the genetic algorithm optimized database showed the best performances for the prediction of quality grade (R 2 =0.76). This study represents a step forward in the use of the machine learning model in the Wagyu beef industry and could pose the base for the construction of a more complex prediction model, which could be used to quantify all the production, sub-production, and waste of the Wagyu industry. Finally, future works will focus on verifying the model response to additional data, and also on expanding the objective variables of the model, to create a precise and comprehensive support tool for the management of Wagyu breeding farms. Declarations Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data and model availability statement The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to their containing information that could compromise the privacy of research participants. Author Contribution P. D.: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing, Data Curation, Software, Visualization, Validation, Formal analysis. G-L. P.: Writing – original draft, Writing – review & editing, Visualization, Validation, Formal analysis. N. S.: Methodology, Writing – review & editing, Data Curation, Software, Visualization, Validation, Formal analysis, Resource. F. M.: Methodology, Writing – review & editing, Visualization, Validation, Supervision. L. N.: Methodology, Writing – review & editing, Visualization, Validation. C. M.: Writing – review & editing, Visualization, Validation, Supervision. K. N.: Conceptualization, Writing – review & editing, Visualization, Validation, Supervision, Project administration, Funding acquisition. Acknowledgement The authors express sincere thanks to Mr. Takahiko Ohmae of Tajima Agricultural High School for his kind help in the data acquisition. Data Availability The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to their containing information that could compromise the privacy of research participants. References Bodirsky BL, Rolinski S, Biewald A, Weindl I, Popp A, Lotze-Campen H (2015) Global Food Demand Scenarios for the 21st Century. 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J Anim Sci 98:. https://doi.org/10.1093/jas/skaa089 Monteiro S do N, Pereira AA, Freitas CS, Serrão GX, de Sousa MAP, Lima ACS, Daher LC da SC, Rodrigues TCG de C, da Silva WC, da Silva ÉBR, Silva AGM e, Bezerra da Silva AS, Silva JAR da, Lourenco-Junior J de B (2024) Machine learning regression algorithms for predicting muscle, bone, carcass fat and commercial cuts in hairless lambs. Small Ruminant Research 236:107290. https://doi.org/10.1016/j.smallrumres.2024.107290 Aizoth Inc. (2023) Multi-Sigma. https://aizoth.com/en/service/multi-sigma. Accessed 4 Sep 2024 RStudio Core Team (2018) R: A language and environment for statistical computing. https://www.R-project.org Russel W.M.S. & Burch R.L. (1959) The Principles of Humane Experimental Technique. Wheathampstead (UK):Universities Federation for Animal Welfare. Available at: https://caat.jhsph.edu/principles/the-principles-of-humane-experimental-technique. Methuen. Shapiro J (2001) Genetic Algorithms in Machine Learning. pp 146–168. https://doi.org/10.1007/3-540-44673-7_7 D’souza RN, Huang P-Y, Yeh F-C (2020) Structural Analysis and Optimization of Convolutional Neural Networks with a Small Sample Size. Sci Rep 10:834. https://doi.org/10.1038/s41598-020-57866-2 Caetano SL, Savegnago RP, Boligon AA, Ramos SB, Chud TCS, Lôbo RB, Munari DP (2013) Estimates of genetic parameters for carcass, growth and reproductive traits in Nellore cattle. Livest Sci 155:1–7. https://doi.org/10.1016/j.livsci.2013.04.004 Brown JE, Fitzhugh HA, Cartwright TC (1976) A Comparison of Nonlinear Models for Describing Weight-Age Relationships in Cattle1. J Anim Sci 42:810–818. https://doi.org/10.2527/jas1976.424810x Dinkel CA, Busch DA (1973) Genetic Parameters among Production, Carcass Composition and Carcass Quality Traits of Beef Cattle. J Anim Sci 36:832–846. https://doi.org/10.2527/jas1973.365832x Kause A, Mikkola L, Strandén I, Sirkko K (2015) Genetic parameters for carcass weight, conformation and fat in five beef cattle breeds. Animal 9:35–42. https://doi.org/10.1017/S1751731114001992 Motoyama M, Sasaki K, Watanabe A (2016) Wagyu and the factors contributing to its beef quality: A Japanese industry overview. Meat Sci 120:10–18. https://doi.org/10.1016/j.meatsci.2016.04.026 Wood JD, Enser M, Fisher AV, Nute GR, Sheard PR, Richardson RI, Hughes SI, Whittington FM (2008) Fat deposition, fatty acid composition and meat quality: A review. Meat Sci 78:343–358. https://doi.org/10.1016/j.meatsci.2007.07.019 Vabalas A, Gowen E, Poliakoff E, Casson AJ (2019) Machine learning algorithm validation with a limited sample size. PLoS One 14:e0224365. https://doi.org/10.1371/journal.pone.0224365 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6653315","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":490713283,"identity":"b33c4cbb-e17f-428e-af7c-913483a01e5e","order_by":0,"name":"Daniele Pinna","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYDCCA4wNQFJCjoEBwiBai4UxTAsReg6AyYrEBiifsBa+281tjwtqJNI33G5u/viDwaKOoBbJOwfbjWcck8jdcOdgmzQPMQ4zuJEIVMkG1AJkMBPlF4iWfxLpQAbIYcRq4W2TSAAyGiSIcpgkyAu8fRKGM8HWGUhINhDSwne7/Zk0z7c6eb4b6Y8//qio4ydoC1o8GBDWQFwKGQWjYBSMghEOACMcOeJ/SR8tAAAAAElFTkSuQmCC","orcid":"","institution":"University of Sassari","correspondingAuthor":true,"prefix":"","firstName":"Daniele","middleName":"","lastName":"Pinna","suffix":""},{"id":490713284,"identity":"e3c1b4a0-151a-4f7f-9aa5-213dc801c98a","order_by":1,"name":"Pablo Guarnido-Lopez","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"","lastName":"Guarnido-Lopez","suffix":""},{"id":490713285,"identity":"634c26f4-796d-455c-9d7e-ede9499d7be1","order_by":2,"name":"Shin-ichi Nagaoka","email":"","orcid":"","institution":"Kyoto University","correspondingAuthor":false,"prefix":"","firstName":"Shin-ichi","middleName":"","lastName":"Nagaoka","suffix":""},{"id":490713286,"identity":"5ef82bed-aab4-4cc8-8f36-4d0d7f85270e","order_by":3,"name":"Moriyuki Fukushima","email":"","orcid":"","institution":"Kyoto University","correspondingAuthor":false,"prefix":"","firstName":"Moriyuki","middleName":"","lastName":"Fukushima","suffix":""},{"id":490713287,"identity":"64c17173-d9b5-4ce6-b4e6-828aa9d23beb","order_by":4,"name":"Nanding Li","email":"","orcid":"","institution":"Inner Mongolia Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Nanding","middleName":"","lastName":"Li","suffix":""},{"id":490713288,"identity":"779a61d8-f950-448b-b8f9-71ad9263e25a","order_by":5,"name":"Maria Caria","email":"","orcid":"","institution":"University of Sassari","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Caria","suffix":""},{"id":490713289,"identity":"e1d1e52a-159b-4230-927f-6d9553f9ce44","order_by":6,"name":"Naoshi Kondo","email":"","orcid":"","institution":"Kyoto University","correspondingAuthor":false,"prefix":"","firstName":"Naoshi","middleName":"","lastName":"Kondo","suffix":""}],"badges":[],"createdAt":"2025-05-13 08:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6653315/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6653315/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87709593,"identity":"42dac968-cb17-48b4-a90f-5a6d087ab727","added_by":"auto","created_at":"2025-07-28 08:25:27","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79033,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the data analysis process. (GA= Genetic Algorithm; CORR = Correlation)\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6653315/v1/5b3dbd141de6b5b505c2ede5.jpg"},{"id":87709594,"identity":"00d39169-3abb-4c38-a8ad-fd5d2addbbbc","added_by":"auto","created_at":"2025-07-28 08:25:27","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":75970,"visible":true,"origin":"","legend":"\u003cp\u003eWeights of each feature class for the prediction of the two summarized scores of the Japanese beef grading system. (GA = Genetic Algorithm; CORR = correlation)\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6653315/v1/ace1cb2b671c856115206a13.jpg"},{"id":87712393,"identity":"3f294fcd-98ec-43c1-ac98-3b46bd0cc9f9","added_by":"auto","created_at":"2025-07-28 08:49:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1190835,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6653315/v1/ce53bcd6-4e89-4145-a31f-992f832929fb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A machine learning approach to predict meat production factors in Japanese black cattle fattening","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe consumption of animal-based protein and animal products is expected to increase by up to 50% by 2050 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Consequently, to fulfill this increasing demand the number of livestock is increasing but the number of farmers and livestock farms are decreasing [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This phenomenon is leading to a great intensification of livestock farms, reducing the ability of farmers to adequately monitor all the animals [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. For this reason, in modern livestock farming, the use of automatic sensors and data analysis systems is becoming prominent, thanks to the introduction of Precision Livestock Farming (PLF). This concept is defined as \u0026ldquo;individual animal management by continuous real-time monitoring of health, welfare, production, reproduction, and environmental impact\u0026rdquo; [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The spreading of PLF technology has been made possible thanks to technological advancements (i.e., communication technologies, IoT) and the reduced cost and dimension of electronic devices [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, currently, PLF is mostly applied in intensive farms, while in small-scale, extensive, or outdoor systems most of the PLF technologies are considered less profitable or even impossible to apply [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNevertheless, one exemption could be represented from animal production with high market value, such as the Japanese \u0026ldquo;Wagyu\u0026rdquo; beef. The Wagyu production system is mostly composed of small-scale farms with a national average of 69.6 heads per farm [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, thanks to its consideration as a \u0026ldquo;gourmet\u0026rdquo; food, Wagyu beef can reach very high prices even in the domestic market (6550\u0026ndash;13290 yen/kg, 42.12\u0026ndash;85.47 euro/kg) with a higher price for specific cuts or exported product [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The most relevant characteristic for the fame and appreciation of Wagyu beef is the \u0026ldquo;Marbling\u0026rdquo; or the ability of the animal to have a high deposition of intramuscular fat, which contributes to the juiciness, flavor, and tenderness of the meat [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. To regulate Wagyu beef production, the Japan Meat Grading Association (JMGA; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.jmga.or.jp\" target=\"_blank\"\u003ewww.jmga.or.jp\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.jmga.or.jp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) established a standard for beef quality evaluation. According to this standard, the carcass is judged based on the surface of the 6th /7th rib cross-section. Then, two summarized indices are calculated to include the meat into 15 categories: Yield Grade (YG) and Quality Grade (QG). YG is calculated by the equation:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:YG=67.37+\\left(0.130RYA\\right)+\\left(0.667RT\\right)-\\left(0.025SCW\\right)-\\left(0.869SFT\\right)+2.049$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere 67.37 represent the basic Yield Grade Score, RYA corresponds to the rib-eye area, RT to rib thickness, SCW to the cold split carcass weight, SFT to the subcutaneous fat thickness and 2.049 is constant included. Then, YG can be classified into 3 grades: A (above average yield or \u0026gt;\u0026thinsp;72), B (average yield or between 69 and 72), and C (below average yield or \u0026lt;\u0026thinsp;69). Moreover, the QG is based on 4 characteristics: Beef Marble Standard (BMS), Beef Color Standard (BCS), meat texture and firmness and Beef Fat Standard (BFS). It is classified into 5 grades: from 5 (highest quality) to 1 (lowest quality; Japan Meat Grading Association, 2023). The score that the carcass receives in the meat evaluation process will strongly influence the price that the final product could reach in the market. For this reason, animals in the fattening period until slaughter (from 10 to 30 months of age) follow a high-concentrate diet, which can reach up to 86% of the total ingestion in the final fattening stage [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Another important factor in achieving a higher level of marbling is serum vitamin A (Vit.A) management. In fact, maintaining a low level of serum Vit.A, especially during the early and middle stages of fattening, shows positive results in the accumulation of intramuscular fat in Japanese cattle breeds [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Although Vit.A deficiency in the diet is acceptable, it could induce poor weight gain, ataxia, blindness, and even death in serious cases [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, intramuscular fat deposition is not only influenced by Vit.A but it is also the consequence of a complex series of variables including sex, genetics, nutrition, and management factors such as weaning/slaughter age, castration, and environmental conditions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThanks to the recent advance of machine learning (ML), it may be possible to analyze all possible factors influencing the meat quality of Wagyu beef and to create an instrument for farmers to determine the most beneficial relationships between these factors achieving the best trade-off between meat quality and animal performance on the feedlot. Moreover, connecting ML to modern PLF sensors could improve the capability of farmers to interpret the large quantity of data obtained by smart devices. These data can contain important information or patterns that ML is more efficient in recognizing. ML has already shown great potential in the prediction of carcass characteristics in beef and sheep (Shahinfar et al., 2019; Aiken et al., 2020; Monteiro et al., 2024). It is also important to consider that in highly fragmented production systems, such as the Wagyu beef industry, it is very difficult to obtain constant and frequent measurements on a large number of animals.\u003c/p\u003e\u003cp\u003eFor this reason, this study aimed to build, test, and optimize an ML model, based on a reduced number of animals, for the prediction of the Japanese meat quality standards using a collection of data obtained by PLF smart sensors.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cem\u003eEthical approval for animal trials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was carried out in strict compliance with the regulation of animal experiments at Kyoto University in the Guide for the Care and Use of Laboratory Animals. The regulation was approved by the Kyoto University Animal Experimentation Committee. The study did not involve endangered or protected species. All efforts were made to minimize animal suffering. The study is reported in accordance with the ARRIVE guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnimals\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 44 clinically healthy Japanese black cattle from\u0026nbsp;Tajima Agricultural High School\u0026nbsp;(Yabu, Japan) were involved in the study. These animals were part of several studies from 2018\u003cs\u003e\u0026nbsp;\u003c/s\u003eto 2023. The animals arrived at the fattening facilities after a weaning period of around 270 days. Animals were fed with a traditional low b-carotene diet, composed mainly of concentrate and an ad libitum access to oaten. In each pen, animals had access to an individual door feeder, water, and minerals. The ground was covered in sawdust which was cleaned monthly. After the completion of the fattening process, animals were driven to the Kakogawa city abattoir to be slaughtered.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe body weight of each animal was evaluated through a calibrated electronic weight scale, every \u0026sim;30 days. Moreover, each animal was equipped with a U-motion (Desamis Ltd., Tokyo, Japan) that measured the minimum (min), average (avg), and maximum (max) levels of temperature, humidity, and temperature humidity index (THI) every 24 hours. The U-motion also measured the time (in minutes) that each animal spent in different activities during the 24 hours. These activities were feeding (Feed_time), moving (Move_time), lying (Lie_time), lying rumination (lying_rumination_time), standing (stand_time), and standing rumination (standing_rumination_time). Finally, each animal was strictly monitored to prevent hypovitaminosis A through practical checking techniques and blood samples. The blood samples were collected every \u0026sim;60 days via jugular venipuncture and the blood composition was measured with high-performance liquid chromatography at the Wadayama Service Center of Hoken Kagaku (Asago, Japan). The measured components of the blood included vitamin A (Vit.A), aspartate aminotransferase (AST), alanine transaminase (ALT), g-glutamyl transferase (GGT), and total cholesterol (T-Cho). A summary of the carcass traits statistics of animals included in the study can be found in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Summary statistics for carcass traits of animals involved in the study.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining set\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest set\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eInitial Database\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTraits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eFinal weight (kg)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e642.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e461.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e762.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e655.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e536.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e714.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e645.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e461.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e762.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eCarcass weight (kg)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e424.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e352.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e486.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e421.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e357.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e470.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e423.3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e352.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e486.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eYield grade\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e74.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e70.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e77.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eQuality grade\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e4.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e4.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e5.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eRib-eye area (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e59.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e41.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e77.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eBeef rib thickness (cm)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e7.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e5.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e9.4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eSubcutaneous fat thickness (cm)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e2.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e1.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e4.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eBeef Marble Standard (BMS)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e8.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e6.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e12.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eBeef Color Standard (BCS)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e3.9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e3.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e5.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eMeat gloss\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e4.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e4.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e5.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eMeat firmness\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e4.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e4.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e5.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eMeat texture\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e4.8\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e4.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e5.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eBeef Fat Standard (BFS)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e2.4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e2.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e4.0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNeural Network\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Artificial Neural Network model (ANN) was built, tested, and optimized with the use of the AI-assisted platform Multi-Sigma (Aizoth Inc., Tsukuba, Japan; Aizoth Inc., 2023) and RStudio (ver. 2023.12.1 build 402; RStudio Core Team, 2018). \u0026nbsp;Furthermore, the structure of the ANN is optimized to reduce overfit and perform analysis with small data sets thanks to the use of AI algorithms that automatically select hyperparameters. This aspect represents a clear advantage for the application of ANN in highly fragmented scenarios and with a limited use of technological tools, where collecting data from a large number of animals is impractical and economically challenging. However,\u0026nbsp;the dataset\u0026nbsp;limitation was carefully weighted to ensure that the study remains robust and provides meaningful insights,\u0026nbsp;and\u0026nbsp;the data limitation was\u0026nbsp;accorded with \u0026ldquo;The Three Rs\u0026rdquo; guiding principle\u0026nbsp;(Russel \u0026amp; Burch, 1959)\u0026nbsp;for appropriate animal experiments. Moreover, 30% of the database, randomly selected, was excluded from the training set and used as an independent test set. The remaining 70% was considered as the training set and fed to the application. At the end of the training, 5 ANN models were created automatically selecting the structure\u0026rsquo;s parameters based on the input data, randomly selecting each time 90% of data for training and 10% for validation. This further segmentation of the data is specifically designed to reduce the overfit, because the configuration ANN is automatically selected to\u0026nbsp;minimize\u0026nbsp;the error between training and validation sets. Also, to improve the prediction performances, all the ANN models were used simultaneously.\u003c/p\u003e\n\u003cp\u003eFurthermore, based on the size and nature of the datasets, two methods of feature selection were tested for optimization:\u003c/p\u003e\n\u003col class=\"decimal_type\" style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003eGenetic algorithm (GA; Shapiro, 2001): The GA is a technique for optimization based on natural selection over multiple generations. It was performed on the \u0026ldquo;yield grade\u0026rdquo; and \u0026ldquo;quality grade\u0026rdquo; results because they included all the other factors of the meat quality evaluation. The hyperparameters used were population size = 400, generation = 200, crossover rate = 0.1, mutation rate = 0.3, evaluator = Adjusted R\u003csup\u003e2\u003c/sup\u003e.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Correlation (CORR): Pearson\u0026rsquo;s correlation was used to select the predictor features that had a higher correlation (absolute value \u0026gt; 0.5) with one of the objective features of the database (output of the model).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe feature selection was performed with the RStudio using the package \u0026ldquo;gaselect\u0026rdquo; and the base function \u0026ldquo;COR\u0026rdquo;. Predictor features selected by each method are shown in Table 2. In \u0026ldquo;Complete\u0026rdquo; (COMPLETE) feature selection was not performed. \u0026nbsp;The process of feature selection is very important to optimize the training time, and the data collection time and to reduce the possible negative impact of irrelevant features in the database. The accuracy measurements for model evaluation were the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), and the R-squared coefficient (R\u003csup\u003e2\u003c/sup\u003e). A summary of the data analysis process is shown in Fig. 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Databases used in the study and predictor features, divided by type, selected by each optimization method.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplete\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(COMPLETE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic Algorithm (GA)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation (CORR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMorphology and development\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eSex\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eInitial_weight (kg)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eMonthly_weight_gain (kg)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eAge_min (month)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eAge_max (month)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eEnvironment\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eTemperature_min (\u0026deg;C)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eHumidity_min (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eTHI_min\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eTemperature_avg (\u0026deg;C)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eHumidity_avg (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eTHI_avg\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eTemperature_max (\u0026deg;C)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eHumidity_max (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eTHI_max\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBehavior\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eFeed_time_total (min)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eFeed_time_avg (min)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eMove_time_total (min)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eMove_time_avg (min)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eLie_time_total (min)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eLie_time_avg (min)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eStand_time_total (min)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eStand_time_avg (min)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eStanding_rumination_time_total (min)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eStanding_rumination_time_avg (min)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eLying_rumination_time_total (min)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eLying_rumination_time_avg (min)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBlood composition\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eVit.A_min (\u003c/em\u003e\u003cem\u003e\u0026mu;\u003c/em\u003e\u003cem\u003eg /dL)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eAST_min\u0026nbsp;\u003c/em\u003e\u003cem\u003e(\u003c/em\u003e\u003cem\u003e\u0026mu;g\u003c/em\u003e\u003cem\u003e\u0026nbsp;/dL)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eALT_min\u0026nbsp;\u003c/em\u003e\u003cem\u003e(\u003c/em\u003e\u003cem\u003e\u0026mu;g\u003c/em\u003e\u003cem\u003e\u0026nbsp;/dL)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eGGT_min\u0026nbsp;\u003c/em\u003e\u003cem\u003e(\u003c/em\u003e\u003cem\u003e\u0026mu;g\u003c/em\u003e\u003cem\u003e\u0026nbsp;/dL)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eT-cho_min (\u0026mu;g /dL)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eVit.A_avg (\u0026mu;g /dL)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eAST_avg\u0026nbsp;\u003c/em\u003e\u003cem\u003e(\u003c/em\u003e\u003cem\u003e\u0026mu;g\u003c/em\u003e\u003cem\u003e\u0026nbsp;/dL)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eALT_avg\u0026nbsp;\u003c/em\u003e\u003cem\u003e(\u003c/em\u003e\u003cem\u003e\u0026mu;g\u003c/em\u003e\u003cem\u003e\u0026nbsp;/dL)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eGGT_avg\u0026nbsp;\u003c/em\u003e\u003cem\u003e(\u003c/em\u003e\u003cem\u003e\u0026mu;g\u003c/em\u003e\u003cem\u003e\u0026nbsp;/dL)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eT-cho_avg (\u0026mu;g /dL)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eVit.A_max (\u0026mu;g /dL)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eAST_max\u0026nbsp;\u003c/em\u003e\u003cem\u003e(\u003c/em\u003e\u003cem\u003e\u0026mu;g\u003c/em\u003e\u003cem\u003e\u0026nbsp;/dL)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eALT_max\u0026nbsp;\u003c/em\u003e\u003cem\u003e(\u003c/em\u003e\u003cem\u003e\u0026mu;g\u003c/em\u003e\u003cem\u003e\u0026nbsp;/dL)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eGGT_max\u0026nbsp;\u003c/em\u003e\u003cem\u003e(\u003c/em\u003e\u003cem\u003e\u0026mu;g\u003c/em\u003e\u003cem\u003e\u0026nbsp;/dL)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003eT-cho_max (\u0026mu;g/dL)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTotal feature\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStatistical and mathematical analyses were performed in RStudio (ver. 2023.12.1 build 402). ANOVA (parametric variables) and Kruskal-Wallis test (non-parametric variables) were used to test differences in the ANN structure and performance and declared significant when P \u0026le; 0.05.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eThe totality of 15 (5 for each database) ANNs generated had similar results in terms of structure, with no statistical difference for any of the tuning hyperparameters (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This outcome can be due to the algorithm\u0026rsquo;s setting for parameter selection, but also to the fact that in ML the principal factor that influences the optimum structure of ANNs is the data nature and not the number of predictor features [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, the models trained with the GA selected features appeared numerically more complex than the others, especially in terms of neurons per layer and epoch of training performed.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of ANNs parameters generated by Multi-Sigma for each dataset.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCOMPLETE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCORR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.6(1.67)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9(1.73)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.2(2.05)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u0026deg; hidden layer\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.2(34.28)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.6(30.98)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35(32.96)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u0026deg; Neurons per layer\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5198.20(1924.66)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7834.4(1834.25)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5060.2(2130.73)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u0026deg; Epoch performed\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2674\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6372\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2813\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7718\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9848\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7382\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u0026minus;b\u003c/sup\u003e Different superscript letters indicate statistical differences among values in the row (P\u0026le; 0.05)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn general, the application of AI-assisted machine learning yielded very promising results, especially considering the sample size included in the study. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the performance of the models on the independent test set for predicting all 13 traits of the Japanese beef grading system. All the ANNs performed similarly, but the COMPLETE database and GA selected features outperformed the CORR based database in 11 of 13 trait predictions, showing a higher accuracy. However, for each of the carcass traits, these models performed differently, which may be due to their different model\u0026acute;s construction itself. The COMPLETE models showed higher accuracy, especially in the yield grade score (RMSE\u0026thinsp;=\u0026thinsp;0.92, MAE\u0026thinsp;=\u0026thinsp;0.71, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.50), the final weight score (RMSE\u0026thinsp;=\u0026thinsp;43.29, MAE\u0026thinsp;=\u0026thinsp;31.94, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.23) and rib-eye area score (RMSE\u0026thinsp;=\u0026thinsp;6.86, MAE\u0026thinsp;=\u0026thinsp;5.14, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.43). This could be explained considering that this COMPLETE model integrated fundamental features for morphology prediction, such as initial weight, or some of the blood biomarkers. In fact, these three traits with better outcomes with the COMPLETE model are related to the initial weight as it is part of the modeling used to forecast the final weight and rib-eye area as previously demonstrated [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In the particular case of the final weight, when prediction models do not incorporate the initial weight, the performance is much less accurate [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The CORR models showed the best performance for the carcass weight (RMSE\u0026thinsp;=\u0026thinsp;30.29, MAE\u0026thinsp;=\u0026thinsp;22.45, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.09) and the beef rib thickness (RMSE\u0026thinsp;=\u0026thinsp;0.90, MAE\u0026thinsp;=\u0026thinsp;0.64, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.14). This may be due to the high phenotypic and genetic correlation (mostly linear) of these two parameters with the rest of the meat quality variables measured [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, for the carcass weight, the R\u003csup\u003e2\u003c/sup\u003e is lower in comparison to the COMPLETE model which obtained similar performance in terms of RMSE and MAE (carcass weight: RMSE\u0026thinsp;=\u0026thinsp;30.67, MAE\u0026thinsp;=\u0026thinsp;22.89, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.17). A higher level of correlation between prediction and observed value could imply a higher possibility of generalization of the model and a consequent lower level of overfitting. Moreover, the GA model resulted to be the most effective in the prediction of quality grade (RMSE\u0026thinsp;=\u0026thinsp;0.37, MAE\u0026thinsp;=\u0026thinsp;0.27, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.76) and most of its components as BMS (RMSE\u0026thinsp;=\u0026thinsp;1.65, MAE\u0026thinsp;=\u0026thinsp;1.43, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.25), Meat gloss (RMSE\u0026thinsp;=\u0026thinsp;0.37, MAE\u0026thinsp;=\u0026thinsp;0.24, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.93), Meat firmness (RMSE\u0026thinsp;=\u0026thinsp;0.39, MAE\u0026thinsp;=\u0026thinsp;0.27, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.75), meat texture (RMSE\u0026thinsp;=\u0026thinsp;0.40, MAE\u0026thinsp;=\u0026thinsp;0.27, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.83) and BFS (RMSE\u0026thinsp;=\u0026thinsp;0.45, MAE\u0026thinsp;=\u0026thinsp;0.32, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.06). The GA model\u0026acute;s construction is related to the creation of new parameters through inputs explaining most of the variability of outputs, therefore, these better relationships observed in meat quality parameters could be due to the fact that Black Japanese cattle\u0026acute;s production system is focused on improving those meat quality parameters [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], which may explain the high level of the variability relating inputs and meat quality outputs. Especially, the good prediction ability of the BMS could represent a very interesting point for Wagyu beef production. In fact, this trait is the most influential on the Japanese beef grading system and is considered to be strongly connected to the animal\u0026rsquo;s diet [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, the collection of feed intake information of individual animals is a challenging task on a farm and consequentially, being able to obtain a reliable prediction of BMS, without knowing the net feed intake, could represent an important advantage of ML system.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrediction accuracy of the neural network model for predicting Japanese black cattle carcass characteristics for the Japanese beef grading system. Root Mean Square Error (RMSE); Mean Absolute Error (MAE); R-squared coefficient (R\u003csup\u003e2\u003c/sup\u003e). A bold value denotes the best score in the row.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScale or unit of measurement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMetric\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCOMPLETE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCORR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eFinal weight\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRMSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e43.92\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e55.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ekg\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMAE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e31.94\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.23\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eCarcass weight\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRMSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e30.29\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ekg\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMAE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e22.45\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.17\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eYield grade\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRMSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.92\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e1-100\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMAE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.71\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eQuality grade\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRMSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.37\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e1\u0026ndash;5\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMAE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.27\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.76\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eRib-eye area\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRMSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e6.86\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ecm\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMAE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e5.14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.43\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eBeef rib thickness\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRMSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.90\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ecm\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMAE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.64\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eSubcutaneous fat thickness\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRMSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ecm\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMAE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.42\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.57\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eBeef Marble Standard (BMS)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRMSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.65\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e1\u0026ndash;12\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMAE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.43\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eBeef Color Standard (BCS)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRMSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.43\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e1\u0026ndash;5\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMAE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.20\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eMeat gloss\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRMSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.37\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e1\u0026ndash;5\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMAE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.24\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.93\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eMeat firmness\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRMSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.39\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e1\u0026ndash;5\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMAE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.27\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.75\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eMeat texture\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRMSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.40\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e1\u0026ndash;5\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMAE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.27\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.83\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eBeef Fat Standard (BFS)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRMSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.45\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e1\u0026ndash;5\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMAE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.06\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIt is important to notice how the GA selection of features performed generally better than the COMPLETE database, while the CORR selected features performed worse than all the other models. These results confirm the ability of GA feature selection methods to allow an optimization of ML without reducing the accuracy of predictions. Another aspect to take into consideration is the \u0026ldquo;composition\u0026rdquo; of the prediction. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the contribution of the different input features, divided by type, for the prediction of the two summarized scores of the Japanese beef grading system. These results reflect the actual knowledge that the final yield or quality of the meat is mostly influenced: (1) by the morphology and development of the animal, which are strongly influenced by his genetical traits, (2) by the behavior, which also includes the time spent in the feeding area and consequentially the ingestion rate, (3) and finally by the blood composition, which is mostly influenced by the breeding techniques and the composition of the feeding (Motoyama et al., 2016). In particular, from the factor analysis, it highlighted how the standing time (comprehensive of standing, standing rumination, and moving), initial weight, monthly weight gain, and the level of aspartate transferase strongly influenced the yield grade, while the quality grade and marbling score resulted more related to the moving time, the level of Vit. A, and the concentration of alanine transaminase. Thus, it is also very interesting to note how the environmental conditions showed a relevant influence on the quality and quantity of meat production.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis work confirmed the ability of ML to predict the carcass traits of Japanese black cattle for Japanese beef grading systems with a good accuracy, taking into consideration that the limited sample size may affect especially the ability of generalization of the prediction model, with the risk of a high specificity and overfitting [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The composition of Wagyu beef is quite different from other breeds, so the development of breed specific grading algorithm could represent an interesting research area for improving the profitability of farms. Nevertheless, To take full advantage of the benefits derived from the use of ANN for Wagyu cattle management the integration into a complete software for animal data use is needed.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, a machine learning model for the prediction of the quality of Wagyu beef was developed, tested, and optimized. In addition, three different methods of feature selection for the construction of neural networks were tested. The performances of the models resulted in a very promising prediction of the score of the Japanese meat grading standards. In particular, the model created with the complete database of 41 features showed a higher accuracy for the prediction of the yield grade (R\u003csup\u003e2\u003c/sup\u003e=0.50) while the genetic algorithm optimized database showed the best performances for the prediction of quality grade (R\u003csup\u003e2\u003c/sup\u003e=0.76). This study represents a step forward in the use of the machine learning model in the Wagyu beef industry and could pose the base for the construction of a more complex prediction model, which could be used to quantify all the production, sub-production, and waste of the Wagyu industry. Finally, future works will focus on verifying the model response to additional data, and also on expanding the objective variables of the model, to create a precise and comprehensive support tool for the management of Wagyu breeding farms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003ch2\u003eData and model availability statement\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to their containing information that could compromise the privacy of research participants.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eP. D.: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review \u0026amp; editing, Data Curation, Software, Visualization, Validation, Formal analysis. G-L. P.: Writing – original draft, Writing – review \u0026amp; editing, Visualization, Validation, Formal analysis. N. S.: Methodology, Writing – review \u0026amp; editing, Data Curation, Software, Visualization, Validation, Formal analysis, Resource. F. M.: Methodology, Writing – review \u0026amp; editing, Visualization, Validation, Supervision. L. N.: Methodology, Writing – review \u0026amp; editing, Visualization, Validation. C. M.: Writing – review \u0026amp; editing, Visualization, Validation, Supervision. K. N.: Conceptualization, Writing – review \u0026amp; editing, Visualization, Validation, Supervision, Project administration, Funding acquisition.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors express sincere thanks to Mr. Takahiko Ohmae of Tajima Agricultural High School for his kind help in the data acquisition.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to their containing information that could compromise the privacy of research participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBodirsky BL, Rolinski S, Biewald A, Weindl I, Popp A, Lotze-Campen H (2015) Global Food Demand Scenarios for the 21st Century. 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Animal 9:35\u0026ndash;42. https://doi.org/10.1017/S1751731114001992\u003c/li\u003e\n\u003cli\u003eMotoyama M, Sasaki K, Watanabe A (2016) Wagyu and the factors contributing to its beef quality: A Japanese industry overview. Meat Sci 120:10\u0026ndash;18. https://doi.org/10.1016/j.meatsci.2016.04.026\u003c/li\u003e\n\u003cli\u003eWood JD, Enser M, Fisher AV, Nute GR, Sheard PR, Richardson RI, Hughes SI, Whittington FM (2008) Fat deposition, fatty acid composition and meat quality: A review. Meat Sci 78:343\u0026ndash;358. https://doi.org/10.1016/j.meatsci.2007.07.019\u003c/li\u003e\n\u003cli\u003eVabalas A, Gowen E, Poliakoff E, Casson AJ (2019) Machine learning algorithm validation with a limited sample size. PLoS One 14:e0224365. https://doi.org/10.1371/journal.pone.0224365\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Machine learning, Japanese Wagyu, Feature selection, Precision livestock farming, Decision Support System, Meat quality","lastPublishedDoi":"10.21203/rs.3.rs-6653315/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6653315/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThanks to its unique characteristics, such as the level of marbling, Japanese Wagyu beef is considered one of the highest quality meats in the world. These characteristics are the result of a wide variety of factors including genetics, production systems, diets, breeding techniques, and environmental conditions. However, the farmer's profit is strongly related to the cost of production (especially the cost of feeding), to the final weight of the carcass and to the quality score that each animal obtains at the slaughterhouse. For this reason, this study aimed to build, test, and optimize a machine learning algorithm for the prediction of individual carcass traits, which could be used by farmers as a support tool. To achieve this result, data on environmental conditions, behavior, and blood composition were obtained from 44 Japanese black cattle raised for beef production. The obtained databases were then optimized through two techniques of feature selection: genetic algorithm and correlation analysis. For each of the resultant databases, a neural network was built and tested. The results showed a promising ability of machine learning algorithms to predict carcass traits with an acceptable accuracy even with a small sample size. Especially, the genetic algorithm optimized database resulted in the best solution, obtaining a higher accuracy (R\u003csup\u003e2\u003c/sup\u003e=0.34) with respect to the complete database (R\u003csup\u003e2\u003c/sup\u003e=0.27) and the correlation optimized database (R\u003csup\u003e2\u003c/sup\u003e=0.09). This study provides a first step forward in the use of machine learning techniques for the optimization of Wagyu beef production.\u003c/p\u003e","manuscriptTitle":"A machine learning approach to predict meat production factors in Japanese black cattle fattening","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 08:25:23","doi":"10.21203/rs.3.rs-6653315/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"87c6cec0-5fd4-4ed8-aa4d-c0eea0485188","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52105331,"name":"Physical sciences/Mathematics and computing/Scientific data"},{"id":52105332,"name":"Physical sciences/Mathematics and computing/Computer science"}],"tags":[],"updatedAt":"2025-07-28T08:25:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-28 08:25:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6653315","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6653315","identity":"rs-6653315","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}